import torch
import torch.nn as nn
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist

def setup(rank, world_size):
    dist.init_process_group("nccl", rank=rank, world_size=world_size)

def cleanup():
    dist.destroy_process_group()

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.fc = nn.Linear(10, 2)  # 示例模型

    def forward(self, x):
        return self.fc(x)

def train(rank, world_size, model, device):
    torch.manual_seed(1234)
    model.to(device)
    model = DDP(model, device_ids=[rank])
    optimizer = optim.SGD(model.parameters(), lr=0.01)

    # 假设的数据加载和训练循环（这里省略具体实现）
    # ...

    # 内存管理策略：
    # 1. 使用梯度累积减少批量大小，从而减少每次迭代的内存使用。
    # 2. 优化数据加载，确保不会一次性加载整个数据集到内存中。

if __name__ == "__main__":
    world_size = 2  # 假设有两个GPU
    mp.spawn(train,
             args=(world_size, SimpleModel(), torch.device("cuda")),
             nprocs=world_size,
             join=True)
